7,346 research outputs found
Performance Guarantees for Distributed Reachability Queries
In the real world a graph is often fragmented and distributed across
different sites. This highlights the need for evaluating queries on distributed
graphs. This paper proposes distributed evaluation algorithms for three classes
of queries: reachability for determining whether one node can reach another,
bounded reachability for deciding whether there exists a path of a bounded
length between a pair of nodes, and regular reachability for checking whether
there exists a path connecting two nodes such that the node labels on the path
form a string in a given regular expression. We develop these algorithms based
on partial evaluation, to explore parallel computation. When evaluating a query
Q on a distributed graph G, we show that these algorithms possess the following
performance guarantees, no matter how G is fragmented and distributed: (1) each
site is visited only once; (2) the total network traffic is determined by the
size of Q and the fragmentation of G, independent of the size of G; and (3) the
response time is decided by the largest fragment of G rather than the entire G.
In addition, we show that these algorithms can be readily implemented in the
MapReduce framework. Using synthetic and real-life data, we experimentally
verify that these algorithms are scalable on large graphs, regardless of how
the graphs are distributed.Comment: VLDB201
Dynamic Time-Dependent Route Planning in Road Networks with User Preferences
There has been tremendous progress in algorithmic methods for computing
driving directions on road networks. Most of that work focuses on
time-independent route planning, where it is assumed that the cost on each arc
is constant per query. In practice, the current traffic situation significantly
influences the travel time on large parts of the road network, and it changes
over the day. One can distinguish between traffic congestion that can be
predicted using historical traffic data, and congestion due to unpredictable
events, e.g., accidents. In this work, we study the \emph{dynamic and
time-dependent} route planning problem, which takes both prediction (based on
historical data) and live traffic into account. To this end, we propose a
practical algorithm that, while robust to user preferences, is able to
integrate global changes of the time-dependent metric~(e.g., due to traffic
updates or user restrictions) faster than previous approaches, while allowing
subsequent queries that enable interactive applications
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